Applied Machine Learning Projects using Scikit-learn

This repository contains a set of applied machine learning projects implemented using Python and Scikit-learn.
The projects cover different supervised learning algorithms including classification, regression, and model evaluation techniques.

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📊 Projects Overview

1. Breast Cancer Classification (SVM)

• Classification of tumor data using Support Vector Machine
• Evaluation using accuracy and confusion matrix

2. Titanic Survival Prediction (Decision Tree)

• Predict survival of passengers using Decision Tree Classifier

3. Iris Classification (KNN + Cross Validation)

• Classification of Iris dataset using K-Nearest Neighbors
• Applied K-Fold Cross Validation for evaluation

4. Employee Attrition Prediction (Logistic Regression)

• Predict employee attrition using Logistic Regression model

5. Employee Classification (Logistic Regression)

• Additional logistic regression classification experiment

6. Heart Failure Prediction (Random Forest)

• Medical dataset classification using Random Forest

7. Boston House Prices Prediction (Linear Regression)

• Regression model for predicting house prices

8. Multivariable Gradient Descent

• Implementation of gradient descent optimization from scratch

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🛠️ Tools & Libraries

• Python
• Scikit-learn
• Pandas
• NumPy
• Matplotlib

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🎯 Objectives

• Practice core machine learning algorithms
• Understand supervised learning workflows
• Apply model evaluation techniques
• Use cross-validation for better generalization
• Work with real-world datasets

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📁 Repository Structure

├── boston_house_prices_linear_regression.ipynb

├── breast_cancer_svm_classification.ipynb

├── employee_attrition_logistic_regression.ipynb

├── employee_logistic_regression_classification.ipynb

├── heart_failure_random_forest_classification.ipynb

├── iris_knn_cross_validation.ipynb

├── multivariable_gradient_descent_optimization.ipynb

├── titanic_decision_tree_classification.ipynb

└── LICENSE

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📌 Author

Moustafa Serag
AI Developer specializing in ML and DL

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🚀 Future Improvements

• Add feature engineering techniques
• Build end-to-end ML pipelines
• Deploy models using Flask / FastAPI
• Add hyperparameter tuning